A Guide to Sentiment Analysis APIs for SaaS Product Managers

As a SaaS product manager, your objective is always two-fold. One, to make sure that your product is the best in the market and has effective differentiation, and second, that your customer service is top-notch so that you can win customer loyalty and retention. 

A sentiment analysis API can be instrumental in helping you ace this business strategy for growth. It extracts high-precision insights from customer experience data that you can use to improve your product and understand sentiment drivers that can help you in better customer targeting.

You can track social media conversations to monitor areas in your product that need improvements so that you are always ahead of the curve as market demands change. A sentiment analysis API also helps you in this way for better brand reputation management. 

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Further in the article, we will find take a look at how you can use this clever AI tool to sharpen your product strategy. But before that, we need to understand what sentiment analysis really is and how it captures the necessary insights you need.

Sentiment analysis is the automatic extraction of sentiment from big data gathered from feedback that people give. This feedback may be from customers or employees and can be through various means such as surveys, emails, social media listening, video blogs, or regular blogs. It could also be from review websites such as Clutch, Google reviews, or TrustPilot, or from forums such as Quora or Reddit. 

To conduct this amazing task of automatically understanding what a text or document is saying in its entirety and whether it’s good or bad news for an organization, the sentiment analysis platform uses many artificial intelligence sub-tasks. This includes machine learning techniques such as natural language processing (NLP) that help the model understand data in everyday language just as a human would. 

Additionally, it uses semantic classification that allows it to group certain words and phrases into categories, named entity recognition (NER) which allows it to recognize who or what (place, brand, currency, etc) is being referenced in the document, text analysis, and several other such machine learning tasks.

With the complex patterns of algorithms working simultaneously and in conjunction with each other, the process in which the sentiment analysis API systems actually works is in straightforward steps.

Step 1 - It gathers data from all sources. On a platform, you can upload this data directly from an excel file, or you can use the URL of the website or video you want. If you are scraping data from social media platforms or using data from your CRM tool, you may use an excel spreadsheet. If you want to analyze a Google review for your SaaS product, you may just use the URL.

Step 2- Once this data is collected, the model will analyze all the text in the data including emojis. If it’s a video, all the audio will be transcribed and turned into text along with the comments that follow the video. The machine learning model will extract topics, themes, and aspects based on the industry it is trained in. 

For example, it will group aspects such as price, rooms, convenience, food, etc. if it is for the hospitality industry and subscription, ease-of-use, speed, etc for a SaaS product.

Step 3 - Now that all this customer data is collected, all of it is assessed for sentiment - positive, negative, or neutral. Aspect-based sentiment analysis is the best of all approaches because it gives you minute details on which aspect of your product or business received positive or negative feedback and why. 

This way, you can point to the exact part of your SaaS business that needs attention instead of making an educated guess and thus make the necessary changes.

Step 4 - All the insights that the model extracts are shown on a visualization dashboard in the form of charts and graphs. This means you can see sentiment trends of your product based on customer demographics. You can also see aspect trends that show how customers are responding to certain aspects of your SaaS product. 

In addition, you can also discover new customer needs and wants from your product, and in general, from what they have expressed in comments and in their responses to open-ended questions in surveys.

Now that we’ve seen how a sentiment analysis API identifies and extracts insights that are the most vital for your business, let’s see how this amazing technology helps you with your SaaS product strategy in a practical way.

Know your customers - Once you have defined the overall business goal for your SaaS product, the most important thing to do is to truly understand your customers. Thorough and precise market research can tell you what kind of customers are your sweet spot based on the strengths and weaknesses of your product line. 

Sentiment analysis of customer feedback and opinions can tell you granular details about what’s working best for you or why you are lacking customer retention, and what you can do about it. After all, repeat customers are the ideal kind.

Understand market needs - Market moods and needs keep changing. And so do customer opinions based on subjective experiences. A sentiment analysis API helps you monitor these hidden sentiment trends so you are not caught unawares. 

Complacency and risky marketing strategies in a highly competitive environment can be detrimental even to behemoths. For example, look at the way the biggest SaaS success story of our time, Netflix, which posed a tough competition to cable companies, is heading for customer woes due to shifting strategies.

Reading the room is very important for any SaaS product, which means there needs to be a holistic strategy to meet changing market needs while balancing internal costs, resources, and service requirements.

Monitor sentiment drivers-  The fundamental question for any SaaS product manager to ask would be what is the single most important factor that makes your SaaS product attractive to buyers. And what are the key factors that could change a potential lead into a sales conversion? 

This is something that another great SaaS product, Australian design company Canva got right when it decided to introduce a design tool that could be used by almost anyone without coding or special skills. Canva understood that small and medium companies were unhappy about having to be dependent on design agencies for small design jobs that did not justify the cost. Knowing sentiment drivers of a select market-based, Canva today has more than 55 million subscribers and is valued at USD 40 Billion. 

Sentiment analysis allows you to follow along these lines and understand market drivers by analyzing social media conversations, news articles, popular memes, blogs, etc. so that you can assess emerging trends yourself.

Go beyond metric-based surveys - Sentiment analysis SaaS empowers you to go beyond net promoter scores, star ratings, and other numerical-based customer satisfaction scores. Qualitative analysis of customer experience data can speak volumes and tell you precisely what people like or dislike about your product or customer service.

While NPS scores or star ratings can give you a general idea of how customers find your SaaS product, only sentiment analysis -powered qualitative assessment of customer experience feedback data can give you action-based insights for product and service improvement.

Monitor competitors - In order to remain on track and relevant in the market, you need to keep a close tab on what’s happening with your competitors. When a sentiment analysis API processes social media comments or reviews or blogs, you can train your model on keywords such as competitor names, hashtags, and such, so that you can glean insights from what customers are talking about your competitor brands, what attracts them to a particular brand, and what you can do to add or improve your product line to close any market gaps you find.

Influence product adoption - Sentiment analysis of product reviews can also tell you a lot about buyer behavior as you analyze sentiment trends based on specific time periods in your data. Cultural, social, and personal subjectiveness play an important role in product adoption as well. But once you are able to understand the granularity of what aspects affect what part of your customer base, you can devise data-backed product strategies.

This means you can make strategic pricing plans for your SaaS product to meet different types of clients, and offer special add-ons to certain demographics in keeping with their particular needs. You could also use influencer marketing and plan an effective B2B SaaS marketing campaign for others. 

Improve customer experience - You’ve already close to winning the game if you have a great SaaS product, but actually going for the gold means ensuring that your product’s performance is pristine. This also includes having a great customer experience and support. 

The biggest customer irk is when technology that is meant to help ends up being a source of frustration. And so, making sure that your SaaS product is without glitches, and if, for whatever reason, there are snags, you have a great, resourceful customer support team that makes sure that all your customers are happy customers. 

Sentiment analysis gives you this power. It allows you to keep getting ongoing feedback from customers so that you are always ahead of the curve when it comes to customer satisfaction.

As you can see from the many examples above, having a keen eye on customer sentiments and market drivers can go a long way in ensuring business success and longevity. As a SaaS product manager, you need to know exactly what kind of marketing campaign you can run that will give you the most return on your investment. You also need to make sure that your product has excellent differentiation from competitors, and that you have an almost real-time indication of fluctuating market sentiment trends due to various business factors. 

Automated, artificial intelligence-enabled sentiment analysis can be a game-changer in this regard. These APIs can be SaaS-based or are available on-prem. They can analyze multiple languages, data types, and data sources, and provide you with all the necessary insights you need for a great product strategy, at your fingertips, at any time.